SAP has today announced the release of several new products to reposition AI as a system of execution, aiming to redefine how enterprise software will be used over the next decade.
Unveiled at SAP Sapphire 2026, the launches intend to replace fragmented copilots with governed, autonomous systems that run real business processes, including customer experience.
For CX leaders, the move signals a shift away from channel‑specific automation toward AI‑driven orchestration across marketing, commerce, sales, and service.
Jessica Keehn, CMO at SAP, told CX Today that SAP is deliberately shielding end users from AI complexity by managing agent coordination behind a unified data foundation.
“The goal for brands should not be to introduce more agents, but to connect the right agents around a shared source of truth so teams can act faster and with less friction,” she explained.
“That complexity should take place behind the scenes, so the user experience feels simple.
“They should be able to state the outcome they want and have the right assistant coordinate the work.”
From Assistance to Execution
SAP’s intentional shift from fragmented, role‑specific AI copilots toward orchestrated, outcome‑driven AI systems that operate across the entire customer lifecycle, will enable enterprises to execute customer journeys end‑to‑end.
Until recently, many CX platforms have introduced AI in pieces, offering individual assistance for each department, but these approaches eventually increase complexity.
These tools often operate on different data sets, optimize for narrow tasks, and require users to manage the handoffs themselves, resulting in more interfaces, more decisions, and more friction for CX teams.
“That complexity should take place behind the scenes, so the user experience feels simple,” Keehn continued.
“A marketer should not have to know which agent can identify a key audience, check inventory, generate content, adapt a campaign, or trigger the next best action. A service team should not have to hunt across systems to understand an order, entitlement, or billing issue.
“They should be able to state the outcome they want and have the right assistant coordinate the work.
Rather than optimizing individual touchpoints in isolation, teams now require consistent, real-time experiences that connect the customer journey, grounding AI in a unified data foundation and hiding coordination logic from the user, allowing CX teams to focus on outcomes.
This allows CX teams to fit the shifting customer expectations beyond traditional omnichannel engagement, which Keehn argues is no longer important on its own.
“Traditional omnichannel experience required being present and consistent across channels: web, mobile, stores, call centers, email, and social. That still matters, but it is no longer enough,” she explained.
“Constant experience requires continuity across touchpoints, context, and operations. Every interaction must reflect the same view of who the customer is, what they need, what they have already done, and what the business can deliver in that moment.”
SAP’s product releases are designed to move AI from isolated assistance toward coordinated execution across the customer lifecycle, introducing a layered architecture that combines data, orchestration, and user experience.
SAP Business AI Platform
As the foundational layer for enterprise AI, the platform aims to address the lack of business context, where 95% of enterprise AI projects fail because AI systems are disconnected from real business processes, data, and rules.
This approach combines SAP Business Technology Platform, SAP Business Data Cloud, and SAP’s Business AI capabilities into a single, governed environment to ensure AI models are trained, deployed, and executed within the same operational context as the business itself.
By enabling AI to work inside the company’s operational system, this will allow the tools to see live business data and understand business rules so actions can be audited and decisions can be governed.
This platform includes SAP Knowledge Graph, a tool that provides AI agents with a clear understanding of processes and relationships across an enterprise’s landscape.
From here, AI agents are able to reason using a shared map, detailing how the organization runs for a safer, more reliable autonomy.
Furthermore, the business AI platform also includes Joule Studio for developers, designed to build, deploy, and manage AI agents to improve access to enterprise-grade AI development while maintaining governance.
Systems of Action
The SAP Autonomous Suite reflects a design shift in core applications, moving toward autonomous process execution within its primary business systems.
Within the system, the suite deploys more than 50 domain‑specific Joule assistants in various business sectors, designed to work within the broader process rather than being isolated.
The Autonomous Close Assistant can compile entries, perform reconciliations, and resolve errors, shortening the financial close process from weeks to days while actively executing work and maintaining auditability and control.
This suite also includes SAP’s plans to expand its industry AI solutions portfolio with eight new industry-specific offerings that use logic, data models, and regulatory requirements to execute start-to-finish processes.
In one customer enterprise case, one of the offerings, Autonomous Asset Management, was able to reduce unplanned downtime by up to 30% without manual coordination.
Redesigning the User Experience
SAP is also offering Joule Work, an AI-first user experience that aims to replace traditional application-driven workflows.
With Joule, users can interact directly by describing the outcome they want to achieve without having to navigate multiple applications or enter data across numerous systems, with this system orchestrating the required workflows, data, and AI agents behind the scenes.
This allows enterprises to move beyond conversational interfaces and proactively surface insights and automate routine tasks, meaning workflows are continuously happening even when users are not actively engaged.
This experience aims to offer consistency across desktop, mobile, and voice interfaces and extend beyond SAP systems into non‑SAP environments.
By handling complexity behind the scenes through orchestration and governance, this aims to make AI‑driven execution feel simple for users.
“For customers, the experience feels seamless,” said Keehn.
“For companies, it means engagement is no longer based on disconnected snapshots but on live business signals.”
Re‑Engineering Transformation
SAP has also introduced agent‑led transformation tooling, aiming to reduce ERP migration effort by approximately 35%.
To ensure faster, more predictable large-scale transformations, these tools can automate system analysis, code remediation, configuration, and testing.
Even after it’s been published, the same tooling continues to optimize code, improve data quality, and manage change to extend AI’s role beyond deployment into continuous improvement.
SAP has also announced the update of RISE with SAP and SAP GROW, embedding AI adoption from day one for enhanced customer use.
A New Foundation for Customer Teams
SAP’s shift toward orchestrated, outcome-driven AI reflects a broader effort to remove complexity from CX technology while increasing its operational value.
This approach aims to place this complexity behind the scenes, allowing users to focus on the outcome they want to achieve, working through the entire customer lifecycle to allow CX teams to move beyond isolated interaction management to execute connected, real-time customer journeys informed by live business signals.
For CX teams, this transforms how work is performed, where AI becomes the responsible owner for surfacing insights, recommending next actions, and executing routine operational tasks.
“Over time, the role of CX professionals will evolve into more strategic, human, experience and empathy-driven roles.” Keehn continued.
“That does not replace the role of humans. It elevates them.”
For CX leaders, this approach signals a structural change in how customer organizations will operate, as success will depend less on managing channels or deploying standalone AI tools, and more on building connected operating models where customer data, operational systems, and AI orchestration work together.
As a result, the competitive advantage will come from how effectively organizations can combine automation with human judgment, using AI to handle complexity while enabling teams to deliver more consistent, proactive, and emotionally intelligent CX.
